#' @title Density Mixed Data Kernel Learner
#' @author RaphaelS1
#' @name mlr_learners_dens.mixed
#'
#' @description
#' Density estimator for discrete and continuous variables.
#' Calls [np::npudens()] from \CRANpkg{np}.
#'
#' @template learner
#' @templateVar id dens.mixed
#'
#' @references
#' `r format_bib("li2003nonparametric")`
#'
#' @template seealso_learner
#' @template example
#' @export
delayedAssign(
  "LearnerDensMixed",
  R6Class("LearnerDensMixed",
    inherit = mlr3proba::LearnerDens,
    public = list(
      #' @description
      #' Creates a new instance of this [R6][R6::R6Class] class.
      initialize = function() {
        ps = ps(
          bws = p_uty(tags = "train"),
          ckertype = p_fct(
            default = "gaussian",
            levels = c("gaussian", "epanechnikov", "uniform"),
            tags = c("train")),
          bwscaling = p_lgl(default = FALSE, tags = "train"),
          bwmethod = p_fct(
            default = "cv.ml",
            levels = c("cv.ml", "cv.ls", "normal-reference"),
            tags = "train"),
          bwtype = p_fct(
            default = "fixed",
            levels = c("fixed", "generalized_nn", "adaptive_nn"),
            tags = "train"),
          bandwidth.compute = p_lgl(default = FALSE, tags = "train"),
          ckerorder = p_int(default = 2, lower = 2, upper = 8, tags = "train"),
          remin = p_lgl(default = TRUE, tags = "train"),
          itmax = p_int(lower = 1, default = 10000, tags = "train"),
          nmulti = p_int(lower = 1, tags = "train"),
          ftol = p_dbl(default = 1.490116e-07, tags = "train"),
          tol = p_dbl(default = 1.490116e-04, tags = "train"),
          small = p_dbl(default = 1.490116e-05, tags = "train"),
          lbc.dir = p_dbl(default = 0.5, tags = "train"),
          dfc.dir = p_dbl(default = 0.5, tags = "train"),
          cfac.dir = p_uty(default = 2.5 * (3.0 - sqrt(5)), tags = "train"),
          initc.dir = p_dbl(default = 1.0, tags = "train"),
          lbd.dir = p_dbl(default = 0.1, tags = "train"),
          hbd.dir = p_dbl(default = 1, tags = "train"),
          dfac.dir = p_uty(default = 0.25 * (3.0 - sqrt(5)), tags = "train"),
          initd.dir = p_dbl(default = 1.0, tags = "train"),
          lbc.init = p_dbl(default = 0.1, tags = "train"),
          hbc.init = p_dbl(default = 2.0, tags = "train"),
          cfac.init = p_dbl(default = 0.5, tags = "train"),
          lbd.init = p_dbl(default = 0.1, tags = "train"),
          hbd.init = p_dbl(default = 0.9, tags = "train"),
          dfac.init = p_dbl(default = 0.37, tags = "train"),
          ukertype = p_fct(levels = c("aitchisonaitken", "liracine"), tags = "train"),
          okertype = p_fct(levels = c("wangvanryzin", "liracine"), tags = "train")
        )

        super$initialize(
          id = "dens.mixed",
          packages = c("mlr3extralearners", "np"),
          feature_types = c("integer", "numeric"),
          predict_types = "pdf",
          param_set = ps,
          man = "mlr3extralearners::mlr_learners_dens.mixed",
          label = "Kernel Density Estimator"
        )
      }
    ),

    private = list(
      .train = function(task) {
        pars = self$param_set$get_values(tags = "train")
        data = task$data()[[1]]

        pdf = function(x) {} # nolint
        body(pdf) = substitute({
          with_package("np", invoke(np::npudens,
            tdat = data.frame(data),
            edat = data.frame(x), .args = pars)$dens)
        })

        kernel = if (is.null(pars$ckertype)) "gaussian" else pars$ckertype
        distr6::Distribution$new(
          name = paste("Mixed KDE", kernel),
          short_name = paste0("MixedKDE_", kernel),
          pdf = pdf, type = set6::Reals$new())
      },

      .predict = function(task) {
        pars = self$param_set$get_values(tags = "predict")
        invoke(list, pdf = self$model$pdf(task$data()[[1]]), .args = pars)
      }
    )
  )
)

.extralrns_dict$add("dens.mixed", function() LearnerDensMixed$new())
